Federated Machine Learning for Translational Research Completed Research

Translational research (TR) is the harnessing of knowledge from basic science and clinical research to advance healthcare. As a sister discipline, Translational informatics (TI) concerns the application of informatics theories, methods, and frameworks to TR. This research builds upon TR concepts, and aims to bring advances in machine learning (ML) and data analytics for improving clinical decision support. A federated machine learning (FML) architecture is proposed to aggregate multiple sources, and intermediate data analytic processes and products to output high quality knowledge discovery and decision making. The proposed architecture is evaluated for its operational performance based on three propositions, and a case for clinical decision support in the prediction of adult Sepsis is presented. Our research illustrates how IS scholarship may provide valuable contributions to the advancement of TI.